Abstract
In this paper, we address the problem of lossless, offline compression of climate data. We propose a technique for compression of climate data using combination of differential encoding and Huffman coding. This technique gives lossless data compression and reduces the number of bits required to encode a set of symbols,thereby leading to high compression. Performance of this method is measured using the compressed file sizes and finding the compression ratio. Our data set consists of three parameters from the Nevada climate data portal – solar radiation, photo synthetically active radiation, and data logger power system voltage.
Also, in this paper a predictor model is proposed to compress solar radiation data using artificial neural networks by applying the differential encoding and Huffman coding method, compression ratios as high as 5.81 for solar radiation data, 5.68 for data logger power system voltage, and 5.11 for photo synthetically active data is achieved. Also, by employing artificial neural network method, a compression ratio of 3.77 for solar radiation data is achieved.
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Acknowledgment
This work is supported (in part) by the Defense Threat Reduction Agency, Basic Research Award # HDTRA1-12-1-0033, and the National Science Foundation (NSF) award #EPS-IIA-1301726. Any findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of NSF.
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Mummadisetty, B.C., Puri, A., Sharifahmadian, E., Latifi, S. (2015). Lossless Compression of Climate Data. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_58
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DOI: https://doi.org/10.1007/978-3-319-08422-0_58
Publisher Name: Springer, Cham
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